Efficient Composite Learning Robot Control under Partial Interval Excitation

Parameter convergence in adaptive control is crucial for improving the stability and robustness of robotic systems. Nevertheless, a stringent condition named persistent excitation (PE) needs to be satisfied to ensure parameter convergence in the conventional adaptive robot control. Composite learning robot control (CLRC) is an innovative methodology that guarantees parameter convergence under a condition of interval excitation (IE) that is strictly weaker than PE. This paper puts forward a time-division multi-channel (TDMC) CLRC strategy such that parameter convergence is achieved even without the IE condition. In the TDMC mechanism, a filtered regressor is integrated with multiple time intervals to generate a generalized prediction error for parameter update, such that excitation information of regres- sor channels at different instants is exploited more effectively and efficiently to achieve fast and accurate parameter estimation. Global exponential stability with parameter convergence of the closed-loop system is achieved under a partial IE condition that is much weaker than IE. Experiments on a collaborative robot with 7 degrees of freedom have demonstrated the superiority of the proposed approach in both parameter estimation and trajectory tracking compared to start-of-the-art approaches.